Abstract
Super-resolution from a single image plays an important role in many areas. However, it is still a challenging work, especially in the high-resolution image’s quality and the algorithm’s efficiency. To obtain high-resolution images, a new single image super-resolution technique that extends existing learning-based super-resolution frameworks is presented in this paper. We don’t use any external example database or image pyramid to learn the missing details, and propose a single image SR method by learning local self-similarities from the original image itself. To synthesize the missing details, we design new filters which based on principles that model the super-resolution process, and use the new filters to establish the HR-LR patch pairs using the original image and its downsampled version. To obtain the SR image, we adopt a gradual magnification scheme to upscale the original image to the desired size step by step. In addition, to control the iterative error, we use the original image to guide the details added. Experimental results demonstrate that the proposed method is very flexible and give good empirical results.
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References
Aly HA, Dubois E (2005) Image up-sampling using total-variation regularizetion with a new observation model. IEEE Trans Image Process 14(10):1647–1659
Baker S, Kanade T (2000) Limits on super-resolution and how to break them. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, volume 2, pages 372–379, Hilton Head Island, SC,USA, 13–15
Ben-Ezra M, Lin ZC, Wilbum B (2007) Penrose pixels: super-resolution in the detector layout domain. In: ICCV
Chang H, Yeung DY, Xiong Y (2004) Super-resolution through neighbor embedding. In: CVPR, volume 1, pages 275–282
Dai S, Han M, Xu W, Wu Y, Gong Y (2007) Soft edge smoothness prior for alpha channel super resolution. In: CVPR, pp 1–8
Fattal R (2007) Image upsampling via imposed edge statistics. ACM Transactions on Graphics 26(3): 95:1–95:8
Freedman G, Fattal R (2011) Image and video upscaling from local self-examples. ACM Transactions on Graphics, Vol. 30, No. 2, Article 12
Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22(2):56–65
Freeman WT, Pasztor E, Carmichael O (2000) Learning low-level vision. Int J Comput Vis 40(1):25–47
Gao X, Zhang K, Tao D, Li X (2012) Joint learning for single-image super-resolution via a coupled constraint. IEEE Trans Image Process 21(2):469–480
Giachetti A, Asuni N (2011) Real-time artifact-free image upscaling. IEEE Trans Image Process 20(10):2760–2768
Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. In: ICCV, pp 349–356
Jamed DF, van Dam A, Steven KF, John FH (1990) Computer graphics, principles and practice, Second Edition. Addison-Wesley
Kim K, Kwon Y (2008) Example-based learning for single-image super-resolution. Pattern Recog Lect Notes Comput Sci 5096:456–465
Kim KI, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. IEEE Int. Conf. Image Graph pp 1127–1133
Lane RO (2010) Non-parametric bayesian super-resolution. IET Radar Sonar Navigat 4(4):629–648
Lin ZC, Shum HY (2004) Fundamental limits of reconstruction-based superresolution algorithms under local reanslation. IEEE Trans PAMI 26(1):83–97
Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2009) Non-local sparse models for image restoration. Proc. Int. Conf. Comput. Vis pp 2272–2279
Morse BS, Schwartzwald D (2001) Image magnification using level set reconstruction. In: CVPR, pp 333–340
Su D, Willis P (2004) Image interpolation by pixel-level data-dependent triangulation. Comput Graph Forum 23(2):189–202
Sun J, Xu Z, Shum H (2008) Image super-resolution using gradient profile prior. In: CVPR, pages 1–8
Sun J, Xu Z, Shum HY (2011) Gradient profile prior and its applications in image super-resolution and enhancement. IEEE Trans Image Process 20(6):1529–1542
Tai YW, Liu S, Brown MS, Lin S (2010) Super resolution using edge prior and single image detail synthesis. In: CVPR, pp 2400–2407
Tang Y, Yuan Y, Yan P, Li X (2013) Greedy regression in sparse coding space for single-image super-resolution. J Vis Commun Image Represent 24(2):148–159
Tipping ME, Bishop CM (2003) Bayesian image super-resolution. In: Backer S, Thrun S, Obermayer K (eds) Advances in neural information processing systems 15. MIT Press, Cambridge, pp 1279–1286
Wang L, Xiang S, Meng G, Wu H, Pan C (2013) Edge-directed single-image super-resolution via adaptive gradient magnitude self-interpolation. IEEE Trans Circ Syst Video Technol 23(8):1289–1299
Yang J, Wright J, Ma Y, Huang T (2008) Image super-resolution as sparse representation of raw image patches. In: CVPR, pp 1–8
Zhang K, Gao X, Tao D, Li X (2012) Single image super-resolution with sparse neighbor embedding. IEEE Trans Image Process 21(7):3194–3205
Acknowledgments
This work was supported by the National Natural Science Foundation of China(No. 61070233,61201323), and Natural Science Foundation projects of Shaanxi Province(No. 2014JQ5189).
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Pan, L., Yan, W. & Zheng, H. Super-resolution from a single image based on local self-similarity. Multimed Tools Appl 75, 11037–11057 (2016). https://doi.org/10.1007/s11042-015-2834-8
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DOI: https://doi.org/10.1007/s11042-015-2834-8